Artificial Intelligence Platform for Trading
Verde Caudalivo processes market data in real time and applies predictive models to reduce emotional bias, slippage and uncontrolled exposure in each trade.
Architecture aimed at low latency, with backtesting processes on extensive historical series and active risk management in each position.
The control panel displays aggregate exposure, real-time drawdown and predictive engine signals in a single view.
The Operational Problem
Even technically experienced traders make systematic errors under pressure: holding losing positions longer than expected, executing late in times of high volatility, and accumulating slippage due to late manual decisions. None of these factors depend on the previous analysis; They depend on the time of execution.
Technical Infrastructure
Verde Caudalivo combines machine learning models trained on live market and historical data with a low-latency execution layer. The objective is not to predict the market with absolute certainty, but to offer a constant probabilistic reading on what the trader operates.
Analyze patterns in the order book, volume and technical indicators to generate short-term movement probabilities. Models are periodically retrained with new market data to limit overfitting and maintain consistency in the face of regime changes.
The execution layer connects directly to the broker's API, reducing the time between signal and order. This limits slippage compared to manual execution, especially in conditions of high volatility or low liquidity.
The panel integrates real-time visualizations: market depth, correlation between portfolio assets and evolution of the accumulated drawdown, allowing the behavior of the model to be verified without the need to interpret code or technical logs.
Risk Management
Risk management does not only apply when opening a position. Verde Caudalivo monitors the account's aggregate exposure continuously, even outside of the trader's active session, and adjusts protection parameters based on the volatility observed in each asset.
Methodology
Every system decision can be traced back to its origin. This level of transparency is deliberate: a model that cannot be explained cannot be audited or judiciously improved.
Real-time market data, order book depth and technical indicators are integrated from multiple sources on a continuous basis.
Statistical and machine learning models process the data to identify patterns with probabilistic relevance.
Each signal is checked against historical data before being activated, adjusting parameters to limit the risk of overfitting.
The order is sent via API with the exposure limits and drawdown already applied, without additional manual intervention.
Frequently Asked Questions
The platform connects using standard API protocols used by brokers that offer programmatic access to their accounts. During the documentation phase, specific compatibility with each client infrastructure is reviewed.
Latency depends on the infrastructure of the connected broker and the geographic location of the execution server. The system design prioritizes minimizing network hops between signal generation and order sending.
Yes. The technical documentation includes the general operation of the models and the backtesting results on historical data, so that the activation decision is made with sufficient information.
No. The system operates through API permissions limited to analysis and order submission, configured by the user in their own broker. Custody of funds always remains in the client's account.
Credentials are stored encrypted and configured with restricted permissions, avoiding as much as possible granting withdrawal permissions when the broker allows it.
Risk limits already applied to open positions remain active at the broker level. The system resumes monitoring as soon as the connection is reestablished, without the need to manually reconfigure parameters.
Next Step
The process begins with a review of your current infrastructure and the risk parameters you wish to apply, prior to any live account connection.
Request API AccessNo credit card is required during the documentation and technical evaluation phase.